In many collaborative webpages designed for online discussion and content sharing, such as online forums, data consumption is limited to unstructured data, inputted as free text by users of the collaborative webpages. Shared information in online discussion platforms is organized in linear lists of posts (based on the information organization methods developed in the early 90s bulletin boards). Such information organization makes data retrieval and consumption problematic, both by humans and machines. For example, often in such exchanges a number of different topics or issues may be raised, yet the format of the discourse (i.e., linear thread based discussion) does not support the presentation of a topical split in a conversation. This makes it hard for users to follow their relevant information and maintain a coherent mental model of the discussed topics. In addition, the lack of structure makes it hard for machines and algorithms to make sense, extract aggregated insights and curate knowledge.
Human data consumption may include retrieving, searching, browsing, reviewing, understanding, editing, combining data from multiple sources and the like. For example, a person that enters a collaborative platform or forum focusing on a specific topic may encounter numerous related discussions on the same topic in different locations and different thread written by different contributors in different contexts. Thus the person is unable to compose a mental knowledge model on the specific topic of interest. This will lead to the lack of a meaningful learning process and generally to lack of new knowledge development. The consumption of the unstructured data, for example by textual search, may be according to keywords or a syntactic search. For example, when searching for side effects of a specific drug, a user is likely to input a search query such as “side effects caused by taking drug X right after taking drug Y”. There may be millions of results for such search query, most of them are irrelevant since the keyword based search ignores the semantics of the query and domain pre-knowledge, and it is very time consuming for users to reveal the relevant side effects and the relevant relation between the specific side effect and the specific drugs, out of this unfocused list of results. The results are displayed in a serial manner according to relevance criteria of keyword search algorithms, and the absence of contextual relations between the results will be left for the user to manually resolve.
Semantic based engines which are focused on extracting semantic relations and tagging out of human curated unstructured content are based on predefined ontologies, templates and rules (when the domain of knowledge is known in advance), or alternatively on machine learning rules and templates which are assigned by automatics algorithms (when there is a poor definition of the domain knowledge in advance). These approaches are very limited in the accuracy of its semantics extraction methods, and in their ability to adapt to new knowledge domains (which then requires new terminologies and predefined rules), because it is not based on semantic relations, placement of content, and structuring of knowledge, which are curated by humans, and thus can enrich the community's knowledge base with high quality and natural language human curated semantics.
One way to improve information visibility and consumption is to display the information in a concept map. The concept map may improve usability of the information on the web page, for example when collaborating data, studying, analyzing, mapping, understanding, examining and the like. A concept map is a diagram showing the relationships among concepts. It is a graphical tool for organizing and representing knowledge. Concepts, usually represented as boxes or circles, are connected with labeled arrows in a networked-branching hierarchical structure. The relationship between concepts can be articulated in linking phrases such as “gives rise to”, “results in”, “is required by,” or “contributes to”.
Concept maps may be used to improve access to content. Concept maps may be generated from a webpage arranged in threads, wherein each stack represents another discussion. The data inputted into the stacks is converted by an entity that purchase the rights for the content into a concept map which is used by the entity to automatically reveal new data from the data inputted as a stack of concepts.